Sensor head

ABSTRACT

An apparatus includes an extendable wand, and a sensor head coupled to the wand. The sensor head includes a continuous wave metal detector (CWMD) and a radar. When the wand is collapsed, the wand and the sensor head collapse to fill a volume that is smaller than a volume filled by the sensor head and the wand when the wand is extended. Frequency-domain data from a sensor configured to sense a region is accessed, the frequency-domain data is transformed to generate a time-domain representation of the region, a first model is determined based on the accessed frequency-domain data, a second model is determined based on the generated time-domain representation, the second model being associated with a particular region within the sensed region, and a background model that represents a background of the region is determined based on the first model and the second model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/322,284, filed Apr. 8, 2010 and titled SENSOR HEAD INCLUDING ATRANSCEIVER; U.S. Provisional Application No. 61/409,899, filed Nov. 3,2010 and titled SENSOR HEAD INCLUDING A TRANSCEIVER; U.S. ProvisionalApplication No. 61/411,759, filed Nov. 9, 2010 and titled SENSOR HEADINCLUDING A TRANSCEIVER; and U.S. Provisional Application No.61/448,869, filed Mar. 3, 2011 and titled OBJECT AND WIRE DETECTION. Thedisclosures of these prior provisional applications are incorporated byreference in their entirety.

TECHNICAL FIELD

This disclosure relates to a sensor head.

BACKGROUND

A large percentage of land mines contain some amount of metal. Manyversions of mines use metal for firing pins, shrapnel, and portions ofthe casing. If a mine has a sufficient quantity of a detectable metal,that mine can be found using a metal detector.

SUMMARY

A collapsible apparatus that includes a sensor head with both a GPR anda continuous-wave metal detector is disclosed. In some implementations,the sensor head also includes a transceiver that is electrically coupledto and in communication with the GPR. Techniques for processing datafrom the GPR to determine whether a low-metal or no-metal threat object(such as small wires associated with explosives or bulk explosives thatinclude little to no metal) are described.

In one general aspect, an apparatus includes an extendable wand, and asensor head coupled to the wand. The sensor head includes a continuouswave metal detector (CWMD) and a radar. When the wand is collapsed, thewand and the sensor head collapse to fill a volume that is smaller thana volume filled by the sensor head and the wand when the wand isextended.

Implementations may include one or more of the following features. TheCWMD may transmit and receive radiation at twenty-one or more differentfrequencies. The radar may be a ground penetrating radar. The groundpenetrating radar may include one receive antenna configured to detectelectromagnetic radiation and one transmit antenna configured totransmit electromagnetic radiation. The ground penetrating radar mayinclude two or more receive antennas, each configured to detectelectromagnetic radiation, and at least one transmit antenna configuredto transmit electromagnetic radiation. The apparatus also may include atransceiver electrically coupled to the receive antenna and the transmitantenna. The transceiver, the receive antenna, the transmit antenna, andthe CWMD may be located in the sensor head. The receive antenna and thetransmit antenna may be located in the sensor head, and the transceivermay be located outside of the sensor head. When the wand and sensor headare collapsed, the apparatus may fill a volume that no larger than aboutthirty-six centimeters (cm) by twenty-six cm by eleven cm.

The apparatus also may include a processor and electronic storage incommunication with the sensor head, and the electronic storage mayinclude instructions that, when executed, cause the processor to accessdata from the CWMD and from the radar, determine a signature of anobject detected by one or more of the CWMD or the radar based on theaccessed data. The apparatus also may include an output deviceconfigured to provide an indication of a detection of an object made byone or more of the CWMD or the radar.

In another general aspect, frequency-domain data from a sensorconfigured to sense a region is accessed, the frequency-domain data istransformed to generate a time-domain representation of the region, afirst model is determined based on the accessed frequency-domain data, asecond model is determined based on the generated time-domainrepresentation, the second model being associated with a particularregion within the sensed region, and a background model that representsa background of the region is determined based on the first model andthe second model.

Implementations may include one or more of the following features. Thesensor may include a ground penetrating radar. Additionalfrequency-domain data may be received from the sensor after determiningthe background model, the additional frequency-domain data may becompared to the background model, it may be determined that theadditional frequency-domain data represents a target based on thecomparison, and an alarm may be triggered based on the determinationthat the additional frequency-domain data represents a target. It may bedetermined whether the first model and the second model includeoutliers. The first model may include a ground coupling model thatrepresents frequencies emphasized by operator motion, and the secondmodel may include a model that represents a surface of the ground andone or more target models, each target model associated with aparticular depth beneath the surface. In some implementations,additional frequency-domain data may be received from the sensor afterdetermining the background model, it may be determined whether theadditional frequency-domain data is an outlier, and the background modelmay be recomputed using the additional frequency-domain data if theadditional frequency-domain data is an outlier.

In another general aspect, a system includes a sensor configured tosense a region at each of multiple frequencies, a processor coupled tothe sensor and an electronic storage, the electronic storage includinginstructions that, when executed, cause the processor to receivefrequency-domain data from the sensor, transform the frequency-domaindata to generate a time-domain representation of the accessedfrequency-domain data, determine a first model based on the accessedfrequency-domain data, determine a second model based on the generatedtime-domain representation, the second model being associated with aparticular region within the sensed region, and determine a backgroundmodel that represents a background of the region, based on the firstmodel and the second model.

Implementations may include one or more of the following features. Thesensor may include a ground penetrating radar. The sensor may include acontinuous wave metal detector (CWMD). The sensor may include a CWMD anda ground penetrating radar. The CWMD may transmit and receive radiationat twenty-one or more different frequencies. The ground penetratingradar and the continuous wave metal detector may be received in a singlesensor head. The sensor is mounted on a platform that is configured tobe held and manually operated by a human operator.

In another general aspect, an apparatus includes an extendable wand, acontinuous wave metal detector (CWMD) configured to radiateelectromagnetic radiation and detect electromagnetic radiation at six ormore different frequencies and coupled to the extendable wand, and aprocessor and an electronic storage coupled to the CWMD, the electronicstorage including instructions that, when executed, cause the processorto access data detected by the CWMD and determine a signature of anobject represented by the accessed data.

Implementations may include one or more of the following features. TheCWMD may be configured to radiate and detect radiation at twenty-one ormore different frequencies.

In another general aspect, an apparatus includes an extendable wand, ametal detector configured to radiated and detect radiation and coupledto the extendable wand, a processor and an electronic storage coupled tothe metal detector, the electronic storage including instructions that,when executed, cause the processor to access data detected by the metaldetector and determine that a non-ferrous object is represented by theaccessed data.

Implementations of the techniques discussed above may include a methodor process, a system or apparatus, a sensor head, a sensor, a kit, orcomputer software stored on a computer-accessible medium. The details ofone or more implementations are set forth in the accompanying drawingsand the description below. Other features will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show plan views of a detection system.

FIGS. 1C and 1D show views of the detection system when collapsed.

FIG. 1E shows a top view of a sensor head used in the detection systemof FIGS. 1A-1D.

FIG. 1F shows a plan view of a cover for the sensor head of FIG. 1E.

FIG. 1G shows views of internal components of the detection system ofFIGS. 1A and 1B.

FIG. 1H shows a view of a wand of the detection system of FIGS. 1A and1B in a collapsed state.

FIG. 1I shows a view of an audio speaker that may be included in thedetection system of FIGS. 1A and 1B.

FIG. 1J shows a view of the system of FIG. 1A in an extended state.

FIG. 1K shows a view of the system of FIG. 1A in a collapsed state.

FIGS. 1L and 1M show a housing used in the system of FIG. 1A.

FIG. 1N shows a plan view of another example sensor head.

FIG. 1O shows a top view of the sensor head of FIG. 1N.

FIG. 2 is an example process for determining a signature of an object.

FIG. 3 is an example process for discriminating among objects.

FIG. 4 is a scatter plot illustrating example feature values formultiple types of clutter and targets.

FIG. 5 shows an example of a multi-path process for analyzing sensordata.

FIGS. 6A and 6B show example data derived from data from the system ofFIG. 1A.

FIG. 7A shows an example process for using frequency-domain data.

FIG. 7B shows another example process for using frequency-domain data.

FIG. 8 shows a computer system for use with the system shown any of theproceeding FIGS.

Like reference numbers refer to like elements.

DETAILED DESCRIPTION

A detection system for scanning a region is disclosed. The region maybe, for example, the surface and subsurface of the ground or a space inthe vicinity of a stationary portal through which persons and objects(such as luggage and cargo) pass. The region may be all or a portion ofa person who is scanned with the detection system by a human operator.The detection system may be used to detect landmines and/or bulkexplosives that are not necessarily included in a landmine. The systemalso may be used to detect metallic objects, such as small wires,objects that may or may not include metal, such as improvised explosivedevices (IEDs), and non-metallic objects, such as explosives that areburied in the ground or obscured by, for example, being hidden on thebody of a person.

The system is lightweight, portable (by, for example, beinghand-carryable and/or wearable), and has a rugged design andconstruction configured to withstand impacts and extreme climateconditions (for example, high winds, rain, snow, ice, and sand). Byemploying integrated electronics, sensor design, and light-weightconstruction techniques (for example, carbon fiber compositeconstruction techniques) the system (which may be referred to asMINI-HSTAMIDS or MINI-H), has reduced size, weight and power compared toprior detection systems, while also having increased structuralintegrity. In some implementations, the system weighs about six pounds(about 2.7 kilograms) and collapses to a 14.3″×10.4″×4.6″ (about 36cm×26 cm×11 cm) volume for belt, hand-carry, or backpack transport.

The sensor head may include radar antennas that transmit and receiveelectromagnetic radiation and are electrically coupled to a transceiver.The radar antennas may be part of a ground penetrating radar (GPR). Thetransceiver may be integrated into the sensor head or may be on thesensor head. In some implementations, the transceiver is locatedseparate from the sensor head but is in communication with the sensorhead. For example, the transceiver may be located in an electronics unitor an electronics housing that is coupled to a wand that is attached tothe sensor head.

Inclusion of the transceiver in the sensor head simplifies cablingrequirements between the sensor head and an electronics unit that isremote from the sensor head. For example, in some implementations, athin, easily coiled universal serial bus (USB) data wire is employedinstead of two relatively thick and long bend-radius coaxial cables.Some prior systems used coaxial cables to communicate data to anelectronics unit separate and removed from the sensor head. For example,in some prior systems, the transceiver was located in a vehicle to whichthe sensor head was mounted. Integration of the transceiver with thesensor head results in the system being collapsible, small, andlightweight. Replacement of the thick non-coiling coax with the thincoiled wires, achievable due to the placement of the transceiver on, in,or near the sensor head, allows for the collapsible design.

Additionally, use of the thin, coil-able data wire may result in greatersystem performance due to the thin data wire providing lower noise datatransmission and lower signal loss as compared to systems that usecoaxial cable for data transfer. The replacement of the coaxial cableswith the thin, coil-able single data cable may result in a two-fold orgreater reduction in false alarm rate.

The sensor head also may include a continuous-wave metal detector(CWMD). The dynamic range of the CWMD allows the GPR and electronicsassociated with the GPR to be housed in the sensor head with the CWMD,integrated into the sensor head along with the CWMD, or otherwise placednear (for example, about a foot or less) the CWMD. Due to the dynamicrange of the CWMD, the CWMD, or data from the CWMD, may be adjusted orotherwise compensated to account for the metal in the transceiver,whereas pulsed metal detectors generally cannot be compensated. Theability of the CWMD to adjust to the transceiver metal allows for thetransceiver to be placed in the sensor head or near the sensor head.Moreover, a CWMD may be able to detect items that a typical pulsed metaldetector is not able to detect, such as non-ferrous metals.

Referring to FIGS. 1A and 1B, the detection system 100 includes a sensorhead 105 attached to a wand 107. A transceiver 127 (FIG. 1E) is includedin the sensor head 105 such that the cabling that carries data to andfrom the sensor head 105 may be simplified. In this example, a cable 109provides data communications between the sensor head 105 and electronics(not shown), such as an electronic storage and an electronic processor,included in a module 111 and/or an electronics housing 118 (FIGS. 1L and1M). The module 111 also may include a speaker 113 or other output (suchas a display, not shown) that provides an indication to an operator ofthe system 100 that a target has been detected.

The system 100 also includes a platform 115 that is sized to fit an armof a human operator or a robotic system. The platform 115 opens on abottom end 117 to a grip 119. The operator of the system 100 may controlthe motion and location of the sensor head 105 by grasping or otherwisecontacting the grip 119 and moving the wand 107 through a range ofmotion. The platform 115 also forms a portion of an electronics housing118.

FIGS. 1C and 1D show views of the detection system 100 when the wand 107is collapsed and the sensor head 105 is folded into the wand 107.

FIG. 1E shows a top view of the components of the sensor head 105without a cover 125 (FIG. 1F). The sensor head 105 includes a GPR 129, atransceiver 127, and a CWMD 133. The GPR 129 includes a receive antenna129 a and a transmit antenna 129 b. The GPR 129 may be astepped-frequency continuous-wave GPR (a GPR with a non-pulsed signal).The low-profile of a stepped-frequency continuous wave (SFCW) GPRantenna configuration allows a reduction in the overall height andcontour of the sensor head 105, making collapse and visual registrationwith the ground easier for the user.

The GPR 129 includes a transmit antenna 129 a and a receive antenna 129b. The transmit antenna 129 a transmits electromagnetic signals in aparticular frequency band, and the receive antenna 129 b receives(detects or otherwise senses) signals from the surrounding environmentthat arise in response to being irradiated with the signals from thetransmit antenna 129 a. The frequency band of the GPR may beapproximately 640 MHz to 4 GHz or any frequency band within thatfrequency range.

The transceiver 127 may be a radar transceiver. The transceiver 127 mayallow for simplified cabling and the elimination of a microwave cablebetween the sensors (such as the GPR 129) in the sensor head 105 andelectronics (such as electronics 135 a and 135 b shown in FIG. 1G) in aseparate part of the detection system. For example, rather than using acoaxial cable or cables, the transceiver 127 allows for a cable such asthe cable 109 (which may be a USB cable) that provides communicationbetween the GPR and electronics that are removed from the sensor head105. Elimination of the microwave cable may result in less powerdissipation and reduction in phase mismatch of the signals traveling inthe microwave cable.

The sensor head 105 may operate in multiple modes, and a particularoperating mode may be selected by the operator of the system 100 throughthe transceiver 127. The transceiver 127 may include afield-programmable gate array (FPGA) or other processor that allowsselection from among multiple operating modes of the sensor head 105 orallows for programming of the FPGA. In some implementations, the usermay manually select between the various operating modes. For example,the user may select an operating mode using an input/output device thatis in communication with the transceiver 127. In some implementations,the operating mode may be selected beforehand.

The operating mode selectable through the transceiver 127 may be a modethat determines operating characteristics of the sensor, or sensors,included in the sensor head 105. For example, each of the operatingmodes of the GPR 129 may be associated with a different frequency band.A first operating mode may be an operating mode in which the GPRtransmits signals in a frequency band from about 640 MHz to 3.4 GHZ, insteps of 20 MHz. Such an operating mode may be used in situations inwhich relatively deep penetration of the GPR signals is desired (such aswhen targets are buried deep in the ground) and when greater resolutionof certain signal processing features (such as a mapping of theground-air interface is desired). Another mode may be an operating modein which the GPR 129 operates by transmitting signals in a frequencyband from 1.3 GHz to 2.7 GHz in steps of 10 MHz. Such an operating modehas a frequency band approximately half as wide as the first mode. Thisoperating mode may be used to, for example, reduce power consumption orto provide more energy (more signals) at a known frequency of interestor more energy in a frequency band of interest.

Although two modes are discussed above, the transceiver 127 may allowselection from among more than two operating modes. For example, a modeof operation may be a mode in which the GPR 129 switches among multipleoperating modes in a predetermined, pseudo random, or random manner. Insome implementations, the transceiver 127 may allow selection of a modebased on environmental conditions.

Referring also to FIG. 1F, a top view of a cover 125 for the sensor head105 is shown. The cover 125 may fit over the components of the sensorhead 105 to protect the components. The cover 125 may attach to the wand107. The cover 125 also may attach to the sensor head 105. The sensorhead 105 may be operated without the cover 125 present.

The sensor head 105 also includes a CWMD 133 that includes an upper coil134 and a lower coil 131. The upper coil 134 may be a coil thattransmits an electromagnetic field and the lower coil 131 may detect anelectromagnetic field generated by currents induced in an object inresponse to being irradiated by the transmitted electromagnetic field.In some implementations, the coil 134 is the coil that detects the EMfield and the coil 131 is the coil that transmits the EM field. The CWMD133 may be placed at or near an outer edge or portion of the sensor head105.

In greater detail, the CWMD 133 produces or transmits an electromagnetic(EM) field at multiple frequencies through the transmit coil 134, andthe produced EM field induces a current in metallic portions of items inthe vicinity of the CWMD 133. The current induced in the metallicportions of the items produces a second EM field that is sensed by areceive coil 131 of the CWMD 133. The second EM field sensed by the CWMD133 is analyzed to further characterize the item. For example, theanalysis may distinguish an item that is a target from an item that is aclutter object or part of the background.

The transmit coil 134 of the CWMD 133 produces EM radiation at a numberof frequencies, and the number of frequencies is sufficient to allowdetermination of a signature of an item that is independent of theitem's orientation relative to the transmit and receive coils 134, 131of the CWMD 133. The CWMD 133 may have more than six separate anddistinct frequencies, or the CWMD 133 may have twenty-one or moreseparate and distinct frequencies.

The CWMD 133 senses quadrature and in-phase (I&Q) data that representsthe second EM field. As discussed with respect to FIGS. 2-4, sensing I&Qdata at multiple frequencies allows determination of a signature of thetarget that is independent of the orientation and/or position of thetarget relative to the sensor. Thus, the signature of the target is thesame, or substantially the same, for the target regardless of theposition or orientation of the target relative to the sensor. Thesignature may allow improved detection of targets and/or improveddiscrimination between targets and clutter. For example, employing thesignature may result in accurate detection of landmines and otherhazardous objects that are buried more than 1-foot (for example,21-inches) below the surface of the ground.

FIG. 1G shows internal components of the detection system 100 of FIGS.1A and 1B. The detection system 100 includes the sensor head 105, thecable 109, the wand 107 (shown in a collapsed state in FIG. 1G),electronics 135 a and 135 b, module 111, and a hand control 137. Theelectronics 135 a and 135 b may, for example, include one or moreprocessors and electronic storage modules that process data from the GPR129 and/or the transceiver 127. The electronics 135 a and 135 b also mayprocess data from a CWMD and other sensors that may be included in thesensor head 105. The electronics 135 a and 135 b may be included in thehousing 118.

The hand control 137 (similar to the hand control 119 shown in FIG. 1A)provides the operator of the system with control over the position ofthe sensor head 105. Additionally, the hand control 137 includes aninterface 139 that allows the user to program the transceiver 127 and/orselect an operating mode for the GPR 129. The hand control 137 also mayallow the user to set various system parameters, such as the volume ortone of a sound that alerts the user to a potential detection.

FIG. 1H shows a plan view of the wand 107 in a collapsed state, and FIG.1I shows a plan view of the module 111. The module 111 includes aspeaker 113 and provides an audio interface to the operator. A headphoneset (not shown) that connects to the module 111 and the speaker 113 maybe included and may be used while operating the system 100 in, forexample, demining operations, along with ancillary hardware. The speaker113 and electronics associated with the speaker 113 support generationof constant and sweeping tones. For example, tones from a sensor (suchas the CWMD 133 or GPR 129) may provide a tone “flip” or other audibleindicator when passing over a target or in response to an item being inthe vicinity of the sensor head 105. The module 111 and speaker 113 alsosupports more complex audio such as human voice. For example, voiceoutputs and other relatively complex tones may be recorded and storedfor playback. The system 100 may include multiple operating modes withthe recorded models for different targets (for example, mines ascompared to improvised explosive devices (IEDs)).

The audio output may be one of three different types: (1) MD output, (2)GPR output or (3) system status output. The MD response sound may be aset of variable pitch and amplitude audio tones, while the GPR soundsmay be discrete, wideband beeps. Other audio responses may be eitherdistinct electronic tones or commands that are generated to inform theoperator of system status through audible indicators alone. For example,a Battery Low Warning command may be generated within five minutes ofbattery life remaining. All (built-in test) BIT Failure debug codes maybe in spoken English. Examples of built-in tests include tests that run,continuously or periodically, to determine whether the GPR and CWMD arefunctioning properly or at all. When the GPR or CWMD are not operatingproperly, the BIT may produce an indicator to the operator of the system100 such that the operator stops using the system 100 and/or repairs thesystem 100.

The system 100 stores the default audio mode and automatic targetrecognition (ATR) models in non-volatile memory enabling the system toremember the states even upon system shut down.

A battery (not shown) may be mounted directly to the rear of the module111, or the system 100 may be powered by a battery that is external tothe system 100. For example, an external battery may be mounted to abelt to form a belt-mounted battery configuration worn by an operator ofthe system 100. The belt-mounted battery configuration may be worn by anoperator of the system 100, and the battery may be coupled to the module111 (or another part of the system 100) to provide power to the system100. A variety of battery types may be employed in the system, forexample, a variety of military batteries may be employed.

An electronic processor included in or on the system 100 (such as in theelectronics housing 118) or in communication with the system 100, may beaccessed through a USB connection. For example, the electronic processormay be accessed at an external battery pack connector interface. Thismay adding flexibility to the system 100. For example, the electronicprocessor may be programmed, reprogrammed, and selectable to addressspecific mine targets (or other specific types of hazardous objects ofinterest) and to address a specific region of operations via web access.

FIG. 1J shows another plan view of the system 100 with the wand 107extended, and FIG. 1K shows another plan view of the system 100 with thewand 107 collapsed and the sensor head 105 folded (or collapsed) againstthe wand 107 to reduce the size of the collapsed system 100.

FIG. 1L shows a glued carbon fiber housing 140, and FIG. 1M shows analuminum housing 145. The housings 140, 145 may be the electronicshousing 118. The housings 140, 145 may house the electronics for thesensor head 105. The aluminum housing 145 allows dissipation of heatgenerated by the electronics housed by the housing 145. Any thermallyconductive, lightweight material may be used to construct the housing145. The housing 145 may be a two-piece housing that is sized to fitabout the wand 107.

FIG. 1N shows a plan view of internal components of another examplesensor head 150, and FIG. 1O shows a top view of the internal componentsof the sensor head 150. The sensor head 150 includes a GPR 155 and aCWMD 159. In some implementations, the sensor head 150 may include atransceiver (not shown) similar to the transceiver 127. The transceiveris in communication with the GPR 155. The transceiver may be located inthe sensor head 150 (similar to the implementation shown in FIG. 1E), orthe transceiver may be located outside of the sensor head 150. Thesensor head 150 also includes cabling, electronics, and a rim to attacha cover similar to those discussed with respect to the sensor head 105.The sensor head 150 may be mounted on a wand such as the wand 107.

The sensor head 150 is similar to the sensor head 105, except the GPR155 included in the sensor head 150 has two receive antennas, 156 a and156 b and one transmit antenna 157. The inclusion of more than onereceive antenna may improve performance by providing more samples of aregion scanned by the sensor head 150. A portion 159 a of the CWMD 159passes between the two receive antennas 156 a, 156 b and the transmitantenna 157.

Although in the example of FIGS. 1N and 1O, the sensor head 150 includestwo receive antennas 156 a, 156 b and one transmit antenna 157, this isnot necessarily the case. The sensor head 150 may include more than tworeceive antennas, and each may be similar to the receive antennas 156 a,156 b, and the sensor head 150 may include multiple transmit antennas,each of which may be similar to the transmit antenna 157.

As discussed above, the system 100 provides a light-weight and portablesensor head. In addition to the various features discussed above, thesystem 100 also may include one or more electronic processors configuredto process data collected by the sensors included in the sensor head 105and the sensor head 150. Data processing techniques are discussed below,and these techniques may be applied to data collected by the sensors inthe sensor head 105 and the sensor head 150. The data processingtechniques discussed below also may be applied to data collected byother sensors. Further, the data processing techniques discussed belowalso may be applied to data as it is collected by a sensor (and may bestored temporarily in a buffer) or to data that was previously collectedand stored in an electronic storage.

The system 100 includes several mechanical aspects. For example, thesystem 100 may be sealed against water and dust. The system 100 mayinclude maintainability improvements that include using an aluminumpiece electronics housing (such as the housing 145 of FIG. 1M) in placeof carbon fiber (such as the housing 140 of FIG. 1L).

Referring to FIG. 2, a process 200 for determining a signature of anobject is shown. The process 200 may be performed by one or moreelectronic processors associated with a sensor head such as the sensorhead 105, the system 100, and/or the sensor head 150. The processor maybe integrated with the sensor head or the sensor head may be separateand removed from the processor. In examples in which the sensor head isseparate from the processor, the processor and the sensor head may be incommunication while the sensor head is operating such that the processorreceives data from the sensor head and analyzes the data as the sensorhead operates. In the example discussed below, the sensor head is orincludes a metal detector capable of sensing quadrature and in-phasedata, such as the CWMD 133 or the CWMD 159. However, in other examples,the sensor head may include a different or additional sensor.

A first magnetic field is produced in the vicinity of an object (210).The object has an orientation relative to a direction of propagation ofthe first magnetic field and the first magnetic field induces a currentin the object. Quadrature and in-phase data representing the secondmagnetic field is sensed as a current arising in a coil of the sensor(220). The sensed data is fit to a two-dimensional signature (230). Thetwo-dimensional signature may be a signature that represents thequadtrature data as a function of the in-phase data.

A template of data that is independent of the orientation of the objectrelative to the first magnetic field is generated (240). The template ofdata also may be independent of an orientation of the object relative toa direction of propagation of radiation produced by the sensor anddirected toward the target. The template of data may be a template thatrepresents a three-dimensional object associated with a two-dimensionalsignature that matches, or closely matches, the two-dimensionalsignature found in (230). The three-dimensional object may be found fromamong multiple candidate three-dimensional objects by iterating throughthe potential three-dimensional space of I & Q data that could projectinto the two-dimensional signature found in (230). The number ofcandidate objects may be reduced by removing non-logical values(non-positive values) until the iteration converges to a uniquecandidate three-dimensional model that projects the two-dimensional I &Q signature found in (230) in real (positive) values.

In the model, the shape and material of each of the metallic objects isdescribed using vectors representing amplitude and frequency, wherefrequency is the relaxation rate of the signature measured after beinginfluenced by the electromagnetic field produced by the sensor. Becausethe three-dimensional model is a close approximation to the detectedobject, the orientation of the detected object relative to the sensormay be accounted for, and the vectors are independent of the relativeorientation of the detected object and the sensor.

A feature of the object is extracted from the three-dimensional template(250). The feature of the object is extracted from data that is derivedfrom, or produced by, the three-dimensional template, such as theamplitude and frequency vectors discussed above.

Extracting a feature of the object may include determining an amplitudeof the second magnetic field and determining a frequency of the secondmagnetic field or the relaxation rate of the detected object after beinginfluenced by the electromagnetic field produced by the sensor.Extracting a feature of the object may include identifying, from thefrequency vector, a first frequency value and a second frequency value.Extracting a feature of the object may include identifying, from theamplitude vector, a first amplitude value and a second amplitude value.In some examples, the feature may include a ratio of the first frequencyvalue and the second frequency value and a ratio of the first amplitudevalue and the second amplitude value. Using the ratio instead of the rawfrequency and amplitude values as the extracted feature values mayremove noise from the value of the feature, particularly if the noise iscommon to all frequency values and/or all amplitude values. The firstand second frequency values may be the two highest frequency values, andthe first and second amplitude values may be the two highest amplitudevalues. The first and second amplitudes may be the amplitudesrespectively associated with the first and second frequencies.

In some examples, a distance between the detected object and the sensormay be estimated. The estimated distance between the detected object andthe sensor may be used to normalize the data collected by the sensor toa constant, arbitrary distance before extracting the feature values ofthe amplitude and frequency. Determining the distance between thedetected object and the sensor allows the extraction and/or use ofadditional features. For example, the distance itself may be used as afeature.

Whether the object is an object of interest is determined based on theextracted features (260). To determine whether the object is an objectof interest, the extracted feature values may be input into one or moreclassifiers that are configured to produce a confidence value that mayassume a range of numerical values, each of which indicates whether theobject is more likely to be a target object or a clutter object. In someexamples, the classifier is configured to produce a confidence valuethat is one of a discrete number of numerical values, each of whichindicate whether the object is an object of interest (a target) or anobject not of interest (clutter).

Although in the example process 200 discussed with respect to FIG. 2,the process includes determining the template of data that isindependent of orientation (such as the three-dimensional object), thisis not necessarily the case. In some implementations, data produced bythe three-dimensional object is received by the processor from apre-generated or separately generated template of data.

Referring to FIG. 3, an example process 300 for discriminating amongobjects is shown. The process 300 may be performed using data producedby the process 300 discussed with respect to FIG. 2. The process 300 maybe performed by a processor integrated with a sensor head such as asensor in the sensor head 105 or the processor may be separate from thesensor head. In examples in which the sensor head is separate from theprocessor, the processor and the sensor head may be in communicationwhile the sensor head is operating such that the processor receives datafrom the sensor head, discriminates, and classifies the data detected bythe sensor head as the sensor head operates.

In the discussion below, multiple classifiers are trained using datathat is known to be associated with targets and data that is known to beassociated with clutter. The training set includes multiple and distincttypes of targets and/or multiple and distinct types of clutter. Eachtarget type is paired, or grouped, with the type, or types, of cluttersthat are most closely associated with the target type. The grouped datais used to train a particular classifier. As a result, this classifieris tuned for the target-clutter pairing, or grouping, such that theclassifier produces a metric or confidence value indicating that anobject that has a feature similar to that of the targets in the targetset is likely, or very likely, to be a target object. The other multipleclassifiers are similarly trained using other clutter-target groupingsor paring. Once trained, each of the classifiers produce, in response toan input representing a value associated with an object of unknownclassification, a metric or confidence value that indicates whether theunknown object is more likely to be clutter or more likely to be atarget. The metric of all of the classifiers may be aggregated toproduce an overall metric for the unknown object. The overall confidencemay produce a more accurate determination of whether the unknown objectis a target as compared to using a single classifier.

In greater detail, a target object set and a clutter object set areaccessed (310). The target object set includes a target that isassociated with a target feature value and a non-target that isassociated with a clutter feature value. For example, the target andclutter feature values may be a ratio of the frequency of relaxation ofa metallic object detected by a CWMD sensor.

Whether the object set includes multiple types of targets is determined(320). The target object set may include multiple and distinct types oftargets (such as different types of landmines, different types of tracechemicals used in the production of explosives, or different types ofmetallic pins used to ignite an incendiary device). Similarly, theclutter object set may include multiple and distinct types of clutter(such as different types of soils in which landmines are buried,different innocuous solids or liquids on which trace chemicals reside,or different types of foot wear in which incendiary devices areembedded). Continuing with the example in which a CWMD sensor is usedfor landmine detection, the sensor may encounter multiple differenttypes of landmines, each having a different shape, size, and/or metalcontent, buried within different types of soils.

Referring also to FIG. 4, a scatter plot illustrating example featurevalues for target sets “A,” “B,” and “C” and clutter sets “E,” “F,” and“G” is shown. To create the scatter plot shown in FIG. 4, feature valuesassociated with each of the targets in the three target sets and featurevalues associated with each of the clutter objects in the three cluttersets are plotted on a two-dimensional graph. In this example, there arethree different target sets and three different clutter sets. In otherexamples, there may be more or fewer clutter and/or target sets, and thenumber of clutter sets and target sets is not necessarily the same. Atarget set (or clutter set) may be considered distinct from anothertarget set (or clutter set) if the two sets do not overlap in featurespace (such as the feature space shown in FIG. 4), or are less than athreshold distance apart. In the example of FIG. 4, “target A” and“target B” are considered to be distinct target types.

If the target set includes one type of target and the clutter setincludes one type of clutter, the process 300 terminates.

A target feature value is compared to a clutter feature value (330). Thetypes of targets and clutters that are closest to each other in thefeature space represented in the scatter plot 400 are grouped or pairedtogether. The targets and clutter may be grouped, paired, or otherwisecompared using, for example, a nearest-neighbor analysis such that aparticular type of target is paired with the clutter that is nearest infeature space. In another example, all target types are grouped with allclutter types that fall within a certain distance of each other infeature space. Regardless of how the target types are grouped with theclutter types, one target type may be associated with one clutter typeor multiple target types may be associated with a lesser number ofclutter types (or visa versa).

The type of target is associated with the type of clutter based on thecomparison (340). As discussed above, the association may be made basedon the closeness of the target type and clutter type in feature space.Referring again to FIG. 4, “target A” is associated, or paired, with“clutter D”, “target B” is associated with “clutter E”, and “target C”is associated with “clutter F.” Although in the example shown in FIG. 4,the paired targets and clutters overlap in feature space, this is notnecessarily the case. In some examples, the paired targets and cluttersmay be close in feature space but not necessarily overlapping. Forexample, clutters and targets may be paired based on being the targetand clutter that are closest to each other as compared to all otherpossible target and clutter pairings or groupings. Closeness in featurespace may be determined by a distance metric such as, for example, aMahalanobis distance, a linear distance metric, or a nearest neighboranalysis.

Multiple classifiers are generated (350). Each of the multipleclassifiers is trained using a particular target-clutter grouping orpairing. The generated multiple classifiers may include various types ofclassifiers. For example, the multiple types of classifiers may includea multi-layer perceptron (MLP), a baysian classifier, radial basisfunction, Kohonen self-organizing map, a simplified fuzzy ARTMAP, and/orsupport vector machine (SVM).

Returning to the example of FIG. 3, “target A” and “clutter D” are usedto train and generate a first classifier, “target B” and “clutter E” areused to train and generate a second classifier, and “target C” and“clutter F” are used to train and generate a third classifier. Thus,each of the generated classifiers is tuned to a particulartarget-clutter grouping or pairing. The classifiers each produce aconfidence value or metric that indicates whether an unknown object is atarget or a clutter based on a feature of the unknown object being inputinto the classifier. For example, the classifier that is trained on“target A” and “clutter D” data may produce a confidence of “1” (or100%) when an unknown object having features similar to those in the“target A” set is received, indicating that the unknown object is atarget. This same classifier may produce a confidence of “0.5” when anunknown object having a feature similar to that of a target in “targetC” is received, indicating that the classifier has made a neutraldecision as to whether the unknown object is a target. By individuallytraining the classifiers in this manner, each of the classifiers is ableto distinguish between clutters and targets that are very close infeature space. Due to their similarities, such targets and clutters maybe difficult to distinguish using ordinary training techniques that donot segment the training data of clutter and/or targets into distincttypes.

The trained classifiers are used to determine whether an unknown objectis more likely to be a target or more likely to be clutter.

A feature value associated with an unknown object (an object that theclassifiers have not encountered previously) is input to the multipleclassifiers (360). The feature value may be, for example, a ratio ofvector frequencies and amplitudes as discussed above. Each of themultiple classifiers into which the feature is input produce a metricthat indicates how likely it is that the unknown object is a target.

The metrics from the multiple classifiers are aggregated into an overallmetric (370). The overall metric may produce improved results ascompared to techniques that determine whether an object is a targetusing a single classifier. The metrics may be aggregated by, forexample, summing the metrics produced by each of the multipleclassifiers. For example, the unknown object may be a target that isassociated with a feature value similar to those of the targets in“target B.” Thus, the first classifier (trained using “target A” and“clutter D”) and the third classifier (trained using “target C” and“clutter F”) may produce a metric that indicates that the classifier isneutral as to whether the target is a clutter or a target. The neutralmetric may be “0.5” on a scale of 0 to 1. In contrast, the secondclassifier (trained using “target B” and “clutter E”) may produce ametric that is very close to “1,” indicating that the unknown object hasa high likelihood of being a target. Thus, in this example, theaggregated metric is the summation of the three metrics, and is “2.” Anunknown object having characteristics of “clutter E” would have anaggregated metric of “1” because the second classifier would produce ametric of “0” and the first and third classifiers would each producemetrics of “0.5.” As a result, the use of multiple classifiers mayimprove performance as compared to techniques that use only oneclassifier. In this example, performance is improved because the metricof the target is further separated from that of the clutter. Moreover,if the feature values for the unknown objects in this example had bothbeen input into the first classifier only, both objects would have theexact same metric of 0.5. As a result, the objects would not bedistinguishable. Accordingly, training multiple classifiers andproducing an overall metric as shown in this example may provideimproved performance as compared to techniques that rely on a singleclassifier trained on non-segmented data.

In examples in which the multiple classifiers include classifiers ofmore than one type, the metric produced by each classifier may benormalized to a common scale. Such a normalization allows the metrics tobe aggregated together without improperly or inadvertently weighting theoutput of a particular classifier as compared to the output of the otherclassifiers.

Whether the unknown object is a target is determined based on theoverall metric (380). The unknown object may be considered to be atarget if, for example, the overall metric exceeds a pre-determinedthreshold value.

In some implementations, whether an unknown object is a target may bedetermined purely from the signal being a specified level above thecomputed background. For example, if the signal exceeds a threshold thatis set based on the background, the signal is deemed to be associatedwith a target. The signal may be based on, for example, the averageamplitude from a subset of 21 frequencies measured by the CWMD.

In some implementations, the shape of the signal is employed in additionto or instead of the average amplitude of the frequencies.Discrimination between targets and clutter may be performed using aSupport Vector Machine (SVM) classifier and a set of features derivedfrom the distribution of the I/Q (for example, real/imaginary) frequencydata measured at a 60 Hz rate by the CWMD sensor. An SVM may be used in,for example, scenarios in which a relatively small amount of data iscollected. The set of features may include signal-to-noise ratio (SNR),the average real component across all frequencies measured by the CWMD,and a set of Discrete Spectrum of Relaxation Frequency (DSRF) values.The DSRF values may be a an amplitude and position vector, and the DSRFvalue of an object is independent of an orientation of the objectrelative to the CWMD. The DSRF values are unique for different types andshapes of metal and thus provide a measure for quantifying the detectionsignature. For example, the mineralized rocks (rocks that have anon-zero metal content) have a flat signature, whereas mines andman-made clutter are curved and/or angled. The DSRF values may be uniquefor different types and shapes of metal and thus provide an measure forquantifying the detection signature. The DSRF values may be computedwith data collected at 15 or more frequencies at which the CWMDoperates. For example, the DSRF values may be computed based on datacollected at 21 frequencies.

The processing discussed with respect to FIGS. 2-4 may be applied todata collected by a CWMD included in the sensor head 105 or the sensorhead 150. As discussed above, the sensor head 105 and the sensor head150 may include a CWMD and a GPR, and data from both of these sensorsmay be analyzed to determine whether a particular detection is a target(such as a mine or an IED).

FIG. 5 shows an example of a process 500 for analyzing data from asensor head that includes a radar and a metal detector. The process 500may be performed by one or more processors associated with the system100, the sensor head 105, or the sensor head 150. The process 500 may beperformed on data that was previously collected by these systems and/orsensors and stored for later use. The process 500 accepts data from aGPR (such as the GPR 129) and a metal detector (such as the CWMD 133),and the process 500 includes GPR-only processing path 505. The GPR-onlyprocessing path 505 is discussed in greater detail in FIGS. 7A and 7B.

To address challenges posed by processing techniques used in some priorsystems, a parallel path is employed so the data from the GPR and theCWMD may be fused or not fused. If the data is not fused, the data fromeach of GPR and the CWMD may be considered to be used independently. Forexample, the process 500 allows GPR-only processing (such as in theGPR-only processing path 505), CWMD-only processing, or both. Othersensors may be used.

The GPR-only processing technique may be optimized for bulk zero-metalor low-metal IED detection and discrimination. The GPR-only processingalarms indicate the presence of a target or potential target on objectsseveral inches or more in size (in any dimension), at any detectabledepth (for example, up to several feet below the surface of the ground),composed mostly of dielectric material, and with low or zero metalcontent. Some implementations provide instant detect alerts over the GPRtargets. In some implementations, the system 100 also may (oralternatively) includes processing that determines whether an improvisedexplosive device (IED) is present.

Operation of the system 100 may be sensitive to the precision of theoperator swing motion as well as to surface artifacts such as footprints and vehicle tracks. Range sidelobes are generated in the range(or time) domain as part of IFFT processing and, although the IEDprocessing analyzes regions separated from (that is, away from) theair/ground interface, the range sidelobes from the air/ground interfacemay still extend into all ranges. As a result, a swing artifact may beinadvertently reported to the user as a detection.

In some implementations, the IED detection processing employs a changedetection aspect that uses principal component analysis (PCA). The PCAmaps the data from the sensor or sensors in the sensor head into a newcoordinate space whereby the first coordinate is in the direction ofmaximum variance, the second coordinate is in the direction of thesecond largest variance, and so on. From the principal componentanalysis, a measured variance is obtained within each newly transformedcoordinate. The measured variance may be used to model the groundclutter. During operation of the system 100, each radar packet may betransformed to the new coordinate space and compared against the modelto determine if the radar packet represents an “outlier”, and if so, achange detection is reported.

In some implementations, two simultaneous change detection algorithmsare run, one focused on the detection of targets and one focused on thechanges occurring specifically at the air/ground interface. By comparingthe responses from these two change detection algorithms, it may bedetermined whether a detection is generated at an appropriate range forthe target or if the detection is another fluctuation. Both swingartifacts and ground surface fluctuations should generate strongerchange detection outputs at the air/ground interface while targetsshould generate a stronger response below the air/ground interface.

The following illustrates how the ratio test acts to reduce potentialfalse alarms. FIG. 6A shows a potential false alarm and swing artifactevident as spikes in the change detection algorithm output.

FIG. 6B shows an example result after performing a ratio test betweenthe target and surface response. In these examples, the swing artifactsas well as strong false alarm response are both reduced.

Referring to FIG. 7A, an example process 700A for detecting items suchas IEDs and small wires is shown. Referring to FIG. 7B, a block diagramof another example process for detecting items such as IEDs and smallwires is shown. The examples shown in FIGS. 7A and 7B may be performedusing data from a ground penetrating radar (GPR), and the process 700Aor 700B may be implemented in the “GPR-only Processing” path 505 shownin FIG. 5.

Each of the processes 700A and 700B may be performed by one or moreelectronic processors included in the system 100 or in communicationwith the system 100, the sensor head 105, or the sensor head 150.

In some implementations, the GPR collects data in the frequency domain.That is, the GPR produces radar signals at multiple frequencies andmeasures an amplitude and/or phase of a signal return from a surface ofthe ground and from items in or under the surface of the ground at eachfrequency. Accordingly, the data from the GPR may be considered to be inthe frequency domain. As discussed below, the frequency-domain data maybe used directly (that is, without being transformed into the timedomain) as part of a determination of whether an item is present on,within, or under a surface of the ground.

The frequency-domain data from the GPR may be used to generate one ormore models that allow suppression of false alarms that may arise fromenvironmental artifacts, such as surface characteristics (for example,tire ruts or footprints), and from motion artifacts, such as artifactscaused by unexpected motion of an operator of the GPR. A false alarm isa detection that is incorrectly classified as a target (such as a buriedIED). Incorrectly classifying a benign object (such as surfaceroughness) as a target may cause a reduction in performance. Forexample, detections classified as targets may be subject to furtherprocessing by an electronic processor and/or examination by an operatorof the system. Thus, the presence of incorrectly classified benignobjects may cause an increase in the amount of time required to scan aparticular area due to, for example, increased processing time. Usingthe frequency-domain data from the GPR as discussed below may result inthe number of false alarms being reduced by a factor two or more,leading to a performance improvement. In some implementations, forexample, the improvement may be three or four fold, or perhaps more.

The techniques discussed with respect to FIGS. 7A and 7B may reduce thenumber of false alarms by generating several ground models during atraining phase and arithmetically combining their Mahalanobis (M)Distance during a testing phase (when additional data is received) toidentify anomalies buried under the ground. Initially, NStepped-Frequency Continuous Wave (SFCW) Ground Penetrating Radar (GPR)frequencies are transmitted into the ground. The signal return (orsurface ground return) of those frequencies is stored asfrequency-domain data and the Inverse Fast Fourier Transform (IFFT) isused to generate a time (range) domain data representation. A set of Kground models are generated, where K is an integer value, and (K-2)number of the K ground models represent signals where targets ofinterest are likely to be found from a range domain standpoint (the K-2models may be referred to Target Ground Models). The remaining twoground models of the K ground models represent signals where sources forclutter are found or are likely found. The two clutter ground modelsources are the ground model representing the ground surface (in therange domain) and a frequency coupling ground model (in the frequencydomain). The frequency coupling ground model represents the frequenciesthat are excited when an operator exhibits swing fluctuations.

All of the K ground models may be analyzed with an outlier rejectionprocessing stage, where the surface ground return is analyzed toidentify if a sample packet is an outlier from the rest of the trainingdata packets. A sample packet or a packet may be a signal return or aground signal return. The mean (average) value and the standarddeviation of the amplitude and/or phase of the signals in each of the Kground models may be computed, and an outlier packet may be anyparticular signal that is one or more standard deviations less than orgreater than the average value. Once outliers have been removed from thedataset, Principle Components Analysis (PCA) processing is performed oneach ground model and the largest V singular values and components aresaved for the testing processing stage, where V is a positive integernumber. Also, a retraining processing phase may be spawned after athreshold number of non-outlier packets have been collected through thetesting phase. The retraining processing phase updates all K groundmodels. The threshold number of non-outlier packets may correspond to anumber of packets that are detected by the system in ten seconds oftypical operation.

During the Testing Phase, each packet is projected into the PCA space ofeach of the K ground models. The M distance of each ground model iscomputed using only the largest V components of the ground models.Finally, a fusion processor computes a Signal-to-Clutter metric based onthe M Distance of all ground models where an arithmetic combination ofthe target ground models is divided by an arithmetic combination of theclutter ground models. A Fused M Distance Metric Threshold is computedusing the Fused M Distance Metric output and an IED detection istriggered via the IED Detection Alert when the Fused M Distance exceedsthe Fused M Distance Threshold for a predetermined period of time.

FIG. 7A shows an example of process 700A used to processfrequency-domain data. The process 700A may be performed by one or moreprocessors included in, or in communication with, a system that includesa GPR, such as the system 100.

Frequency-domain data that represents a spatial region is accessed(701). The frequency-domain data may include, for example, an amplitudeand phase of a radar return generated by directing multiple-frequencyradiation from a ground penetrating radar at the surface of the Earthand detecting the return that is reflected from the surface andsubsurface regions at each of the multiple frequencies. The generatedradiation and the reflected return may include, for example, 140discrete frequencies or discrete frequency bands.

The frequency-domain data may be accessed from an electronic storagethat stores frequency-domain data collected during a previous datacollection, or the frequency-domain data may be data that is collectedby the GPR and stored in a temporary buffer for subsequent, throughnear-real time, analysis. In some implementations, the frequency-domaindata is accessed by being provided by the system 100 to a separateelectronic processor for analysis.

Time-domain data representing the spatial region is generated from thefrequency-domain data (702). The spatial region may be the surface ofthe ground and regions beneath the surface of the ground to a depth ofpenetration of the radar signal. The time-domain data may be generatedby, for example, performing an inverse Fourier transform on thefrequency-domain data. The time-domain data represents an amplitude ofthe radar signal as a function of time. Because the time for the radarreturn signal to reach the detector correlates with the depth from whichthe return signal emanates, the time-domain data also may be referred toas range-domain data that represents the strength of the radar return asa function of depth beneath the surface (or distance from the sensor).

A first model is determined based on the accessed frequency-domain data(703). The first model may be referred to as a coupling ground model,and the first model is in the frequency domain. The coupling groundmodel identifies those frequencies (within the multiple frequencies thatare included in the radar signal) that are excited in response tooperator-induced artifacts in the data, such as artifacts caused byoperator jitter and/or incorrect operator motion of the sensor. Asdiscussed below, the first model is used to divide, reduce, minimize, orremove the frequencies that are excited or otherwise enhanced byunexpected operator motion. In other words, the first model is used toreduce or eliminate clutter that arises from the motion of an operatorof the system 100.

A second model is determined based on the generated time-domain data,and the second model is associated with a particular range within thespatial region (704). The second model may include more than one model,and, the total number of first and second models together may be aninteger number “K.” Collectively, the first and second models may bereferred to as the “K ground models.” The second model includes a modelof the surface of the ground (“surface ground model”), which isdetermined from time-domain (range-domain) data that is reflected fromthe surface. The surface model includes one or more time-domain signalsthat are representative of a signal returned from the surface of theground.

The second model also may include one or more models that are associatedwith a particular sub-surface region. Each of these models may bereferred to as a “ground target model,” and each model representssignals where targets of interest are likely to be found from arange-domain perspective. For example, one model may represent a regionfrom just below the surface to a depth of several inches, and anothermodel may represent a region that is deeper than a depth of severalinches.

An initial background model is generated based on the first model andthe second model (705). The background model represents a background ofthe region, and the background may be considered to be everything in theregion other than targets. Thus, the background includes naturalfeatures such as soil and rocks, and the background may vary with theenvironment. To determine the background model, each of the K groundmodels may be processed with principal components analysis (PCA) todetermine which of the frequencies contributes the most to the K groundmodels (that is, those frequencies that tend to change the most in thepresence of a background or a target). The largest “V” singular valuesand components of the K ground models from the PCA are stored for lateruse, where “V” is a positive integer value. In some implementations,each of the K ground models is analyzed for outliers, and any outliersare removed before the initial background model is generated.

An additional frequency-domain signal is received (706). The additionalfrequency-domain signal may be received after the initial backgroundmodel is generated. For example, the additional frequency-domain signalmay be radar returns received as the system 100 travels through a regionto scan the region for IEDs. The additional frequency-domain data may beany raw sensor data that is in the frequency-domain. In someimplementations, the additional frequency-domain signal is analyzed todetermine whether the additional frequency-domain signal is an outlier.When the number of additional frequency-domain signals that are notoutliers exceeds a predetermined number, each of the K ground models arerecomputed. Because the background may change when, for example, thesystem 100 travels from a region that is unimproved to a region (such asa paved road) that is improved, recalculating the K ground models mayfurther improve performance. The number of non-outliers needed to exceedthe predetermined number may correspond to the number of data packets(or return signals) received in a predetermined time under typicaloperating conditions of the GPR (or other sensor). For example, thepredetermined number may be set such that the K ground models areupdated, for example, approximately every few seconds, approximatelyevery few minutes, or approximately every hour. In some otherimplementations, the update time may vary according to the environmentin which the system 100 is operating. If the environment remainsgenerally unchanged, then updates to the K ground models are relativelysmall.

The additional frequency-domain signal is compared to the initialbackground model (707) to determine how similar the additionalfrequency-domain signal is to the initial background model. A greaterdissimilarity indicates a higher likelihood that the additionalfrequency-domain signal represents a target rather than the background.In some implementations, to determine the similarity between thebackground model and the additional frequency-domain signal, theMahalanobis distance (M-distance) of each of the K ground modelscomputed using only the largest “V” components of the K ground modelsfound in (705). A signal-to-clutter metric (SCM) based on the M-distancemay be determined. In some implementations, the signal-to-clutter ratiois determined from an arithmetic combination of the target ground modelsis divided by an arithmetic combination of the clutter ground models(the surface ground model and the coupling ground model) according toEquation 1:SCM=SUM(target ground models)/(surface ground model+coupling groundmodel).

Whether the received signal represents a target is determined (708).Application of a threshold to a metric, such as the M-distance and/or tothe SCM may indicate whether a target is present or not. If the metricis above or equal to the threshold, a target is present. If a target isdetermined to be present, an alarm may be activated. The alarm may be,for example, a visual alarm and/or an audible alarm. In someimplementations, the alarm is activated when the metric exceeds thethreshold for a predetermined amount of time.

Referring to FIG. 7B, a block diagram of another example process 700B isshown. The example process 700B includes a training phase 712, a testingphase 713, and a retraining phase 714. Sensor data that includes data atN different frequencies is input into the training phase 712. The sensordata may be data from a ground penetrating radar. An inverse Fouriertransform of the sensor data is performed (710), and the resulting time(range) domain data is used to determine K number of ground models 715.In this example, K is a positive integer greater than two. The K groundmodels 715 include a surface ground model 716, (K-2) target groundmodels 717, and a coupling ground model 718.

The surface ground model 716 is a time-domain model of radar signalsreflected from the surface of the ground. Each of the target groundmodels 717 includes signals from a particular subsurface region. Forexample, one target ground model may represent a region one to twocentimeters below the surface and another target ground model mayrepresent a region seven to ten centimeters below the surface. Thecoupling ground model 718 is a frequency-domain model derived directlyfrom the sensor data, and the coupling ground model 718 represents thefrequencies that are excited when, for example, an operator of thesystem 100 causes unexpected swing fluctuations. Together, the surfaceground model 716 and the coupling ground model 718 may be considered“ground clutter models.”

The K ground models are analyzed with an outlier rejection process 720.The surface return signals in each of the K ground models is analyzed todetermine whether a particular signal is an outlier compared to theremaining signals. Any outliers that are found are removed from the Kground models, and the K ground models are analyzed with principalcomponent analysis (PCA) 730. The PCA processing determines the largest“V” singular values and components (the frequencies that cause the mostvariance in the data), and these components are saved for the testingphase 713. The initial ground model 740 is generated based on theresults of the PCA. The initial ground model 740 is made available foruse during a testing phase 713.

During the testing phase 713, additional sensor (GPR) data, similar tothe sensor data used in the training phase 712 but collected at a latertime, is analyzed to determine whether the additional sensor dataincludes a target (such as a buried IED or a small wire used to detonatean explosive). A PCA projection/M distance computation module 750performs a PCA projection and determines an M distance for each of the Kground models. Each packet of GPR data is projected into the PCA spaceof each of the K ground models, and the M distance of each ground modelis computed using only the largest “V” components found during the PCAprocessing of the K ground models. A fusion processor 760 determines asignal-to-clutter metric (SCM) based on the M distance of each of the Kground models. The SCM may be determined using Equation 1. A thresholdis applied to an output 770 of the fusion processor 760. If the output770 meets or exceeds the threshold, the GPR data packet is sufficientlydifferent from the background to be deemed a target. Otherwise, the GPRdata packet is not deemed a target.

In some implementations, a retraining phase 714 is triggered when athreshold number of non-outlier data packets are received. The recomputeground models module 780 causes the K ground modules 715 to beregenerated based on data that the GPR is currently, or has recently,produced.

Although the examples discussed with respect to FIGS. 7A and 7B areprimarily related to GPR, the processing is applicable to other types ofsensors as well. Further, the IED processing discussed above may be usedin combination with other GPR processing techniques and techniques usedto process data from other sensors.

The above examples discuss the benefits of example processing techniquesthat, when applied to GPR data, may improve detection of IEDs and smallwires. Additionally, a CWMD, used alone or in combination with a GPR,also may be used to detect IEDs and small wires.

Some IED threats contain minimum or zero metal content as well as typesof metal that are not easily detectable—if at all—by available pulsedmetal detector (MD) systems. The continuous wave metal detector (CWMD),such as the CWMD discussed above, may provide detection of metals andfusing methods that pulsed systems cannot. For example, the CWMD maydetect non-ferrous metals such as many types of stainlesssteel/titanium, wires used to detonate IEDs (for example, simple speakerwire), and threats that may be hidden or shadowed by neighboring metalin doorways or simply by metal debris. In some implementations, the CWMDtransmits over twenty-one frequencies (in a frequency range of, forexample, 300 Hz-90 kHz) through a dedicated transmit coil andcontinuously receives all frequencies using a dedicated receive coil.The CWMD's 24-bit dynamic range conversion supports compensating forboth fixed nearby metal (such as the GPR antennas and the sensor headmounted transceiver board) as well as dynamic sources, such asmineralized soils, in real time.

The coherent MD (or CWMD) design also achieves may achieve lower noiseoperation and may allow the generation of an expanded feature space viaprocessing of in-phase (I) and quadrature (Q) signals just as from theGPR. This may provide increased performance, and this additionalinformation provides improved discrimination.

In some implementations, the data from the CWMD may be determined asdetections purely from the signal being a specified level above thecomputed background. The signal may be computed as the average amplitudefrom a subset of twenty-one frequencies measured.

FIG. 8 is a block diagram of a computer system 800 that can be used inthe operations and systems described above, according to oneimplementation. The system 800 includes a processor 810, a memory 820,an electronic storage 830 and an input/output interface 840. Each of thecomponents 810, 820, 830 and 840 are interconnected using a system bus850. The processor 810 is capable of processing instructions forexecution within the system 800. In some implementations, the processor810 is a single-threaded processor. In another implementation, theprocessor 810 is a multi-threaded processor. The processor 810 iscapable of processing instructions stored in the memory 820 or on theelectronic storage 830 to display graphical information for a userinterface on the input/output interface 840. The processor 810 may becoupled to another element, such as a sensor within the sensor head 105,150 by being electrically coupled to the sensor and able to exchangedata and signals with the sensor.

The memory 820 stores information within the system 800. In oneimplementation, the memory 820 is a computer-readable medium. In anotherimplementation, the memory 820 is a volatile memory unit. In stillanother embodiment, the memory 820 is a non-volatile memory unit.

The electronic storage 830 is capable of providing mass storage for thesystem 800. In one embodiment, the storage device 830 is acomputer-readable medium. In various different embodiments, the storagedevice 830 may be a floppy disk device, a hard disk device, an opticaldisk device, or a tape device.

For example, the system 100, discussed previously with respect to FIGS.1A-1E, may include the processor 810 executing computer instructionsthat are stored in one of memory 820 and storage device 830.

The input/output device 840 provides input/output operations for thesystem 800. In one implementation, the input/output device 840 includesa keyboard and/or pointing device. In another implementation, theinput/output device 840 includes a display unit for displaying graphicaluser interface as discussed above.

The techniques can be implemented in digital electronic circuitry, or incomputer hardware, firmware, software, or in combinations of them. Thetechniques can be implemented as a computer program product, that is, acomputer program tangibly embodied in an information carrier, in amachine-readable storage device, in machine-readable storage medium, ina computer-readable storage device, in computer-readable storage medium,or in a propagated signal, for execution by, or to control the operationof, data processing apparatus, such as a programmable processor, acomputer, or multiple computers. A computer program can be written inany form of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program can bedeployed to be executed on one computer or on multiple computers at onesite or distributed across multiple sites and interconnected by acommunication network.

Method steps of the techniques can be performed by one or moreprogrammable processors executing a computer program to performfunctions of the techniques by operating on input data and generatingoutput. Method steps can also be performed by, and apparatus of thetechniques can be implemented as, special purpose logic circuitry, on,for example, an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for executing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, such as,magnetic, magneto-optical disks, or optical disks. Information carrierssuitable for embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, such as, EPROM, EEPROM, and flash memorydevices; magnetic disks, such as, internal hard disks or removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated inspecial purpose logic circuitry.

A number of implementations of the techniques have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the claims. Forexample, useful results still could be achieved if steps of thedisclosed techniques were performed in a different order and/or ifcomponents in the disclosed systems were combined in a different mannerand/or replaced or supplemented by other components.

For example, although the system shown in FIG. 1A is a handheld systemintended to scan the surface of the ground, the sensor head 105 may bemounted on a fixed platform (such as a portal) through which personspass and are scanned for harmful objects. The sensor head 105 may beused to scan persons or luggage for explosives, small wires, and metal.

Instead of being mounted on the wand 107, the sensor head 105 may bemounted on a vehicle, a platform that is manually or roboticallyoperated, or a movable cart. In these implementations, the cable 109 isused to communicate data to and/or from the sensor head 105 toelectronics associated with the vehicle, platform, or movable cart. Thesensor head 150 also may be mounted in any of these configurations andused in any of these situations.

In some implementations, the wand 107 may be non-collapsible. Forexample, the wand 107 may be a fixed-form wand used to scan humanpersons for hazardous objects. The sensor head 105 may be mounted on afixed platform such that the sensor head 105 scans objects as theobjects pass through the range of the sensors in the sensor head 105.

The sensor head 105 may include the CWMD 133 without a GPR or other typeof radar, and the sensor head 105 may include the CWMD 159 without a GPRor other type of radar. In yet other implementations, the sensor head105 includes only the GPR 129 and the transmitter 127 that allows forsimplified cabling.

The processing techniques discussed with respect to, for example, FIGS.2, 3, 4, 7A, and 7B may be applied to data collected by and receivedfrom the sensors included in the sensor head 105 but stored for lateranalysis or the processing techniques may be applied during operation ofthe system 100 and the sensor head 105.

What is claimed is:
 1. A method comprising: accessing frequency-domaindata from a sensor configured to sense a region; transforming thefrequency-domain data to generate a time-domain representation of theregion; determining a first model based on the accessed frequency-domaindata; determining a second model based on the generated time-domainrepresentation, the second model being associated with a particularregion within the sensed region; and determining a background model thatrepresents a background of the region based on the first model and thesecond model.
 2. The method of claim 1, further comprising: receivingadditional frequency-domain data from the sensor after determining thebackground model; comparing the additional frequency-domain data to thebackground model; determining that the additional frequency-domain datarepresents a target based on the comparison; and triggering an alarmbased on the determination that the additional frequency-domain datarepresents a target.
 3. The method of claim 1, wherein accessingfrequency-domain data from a sensor configured to sense a regioncomprises accessing the frequency-domain data from a ground penetratingradar configured to sense the region.
 4. The method of claim 1, furthercomprising determining whether the first model and the second modelinclude outliers.
 5. The method of claim 1, wherein the first modelcomprises a ground coupling model that represents frequencies emphasizedby operator motion, and the second model comprises a model thatrepresents a surface of the ground and one or more target models, eachtarget model associated with a particular depth beneath the surface. 6.The method of claim 1, further comprising: receiving additionalfrequency-domain data from the sensor after determining the backgroundmodel; determining whether the additional frequency-domain data is anoutlier; and re-computing the background model using the additionalfrequency-domain data if the additional frequency-domain data is anoutlier.
 7. A system comprising: a sensor configured to sense a regionat each of multiple frequencies; a processor coupled to the sensor andan electronic storage, the electronic storage comprising instructionsthat, when executed, cause the processor to: receive frequency-domaindata from the sensor; transform the frequency-domain data to generate atime-domain representation of the accessed frequency-domain data;determine a first model based on the accessed frequency-domain data;determine a second model based on the generated time-domainrepresentation, the second model being associated with a particularregion within the sensed region; and determine a background model thatrepresents a background of the region, based on the first model and thesecond model.
 8. The system of claim 7, wherein the sensor comprises aground penetrating radar.
 9. The system of claim 7, wherein the sensorcomprises a continuous wave metal detector (CWMD).
 10. The system ofclaim 9, wherein the sensor further comprises a ground penetratingradar.
 11. The system of claim 10, wherein the ground penetrating radarand the continuous wave metal detector are received in a single sensorhead.
 12. The system of claim 7, wherein the sensor is mounted on aplatform that is configured to be held and manually operated by a humanoperator.
 13. The system of claim 11, wherein the CWMD transmits andreceives radiation at twenty-one or more different frequencies.